GB2598681A - Scheduled thermal control system - Google Patents

Scheduled thermal control system Download PDF

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Publication number
GB2598681A
GB2598681A GB2116805.9A GB202116805A GB2598681A GB 2598681 A GB2598681 A GB 2598681A GB 202116805 A GB202116805 A GB 202116805A GB 2598681 A GB2598681 A GB 2598681A
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United Kingdom
Prior art keywords
schedule
refrigeration system
enclosure
candidate schedules
model
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Granted
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GB2116805.9A
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GB2598681B8 (en
GB202116805D0 (en
GB2598681B (en
GB2598681A8 (en
Inventor
Gerald Wolf Elliott
Woolf Woolf James
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Lineage Logistics LLC
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Lineage Logistics LLC
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Publication of GB2598681B8 publication Critical patent/GB2598681B8/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Devices That Are Associated With Refrigeration Equipment (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

Refrigeration management includes determining an optimal operational schedule to control a refrigeration system for a cold storage facility. Various approaches can be used to determine an operational schedule with an optimal operational outcome that satisfies constraints representative of a range of factors, such as thermal characteristics of a refrigeration management system, energy cost, and environmental factors external to the system, which can affect refrigeration management of a cold storage facility.

Claims (21)

1. A method for determining an operational schedule to control a refrigeration system for an enclosure, the method comprising: determining a thermal model of the enclosure and the refrigeration system, the thermal model modeling one or more thermal properties of the enclosure and the refrigeration system under varied use and environmental conditions; obtaining an energy cost model, the energy cost model including a schedule of projected energy costs for a predetermined period of future time; obtaining an environmental model, the environmental model including one or more projected external environmental conditions in a geographic area where the enclosure is located for the predetermined period of future time; determining the operational schedule to control the refrigeration system over the predetermined period of future time by: generating a plurality of candidate schedules for controlling the refrigeration system for the predetermined period of future time, the plurality of candidate schedules determined based on the thermal model, the energy cost model, and the environmental model, wherein each of the plurality of candidate schedules provides a different schedule of, at least, operation levels for the refrigeration system over the predetermined period of future time; generating a multi-dimensional graph providing costs for cooling the enclosure according to the plurality of candidate schedules, wherein each of the costs represent a combination of an energy cost and an energy consumption according to each of the plurality of candidate schedules; randomly selecting a seed schedule from the plurality of candidate schedules; evaluating the seed schedule in the multi-dimensional graph using an iterative optimization algorithm; and selecting the operational schedule that provides an optimal cost from among the plurality of candidate schedules, the optimal cost corresponding to a local minimum of the costs identified when starting with the seed schedule in the multi-dimensional graph; and controlling the refrigeration system over the predetermined period of future time according to the determined operational schedule.
2. The method of claim 1, where the operational schedule is determined further by: evaluating the costs for cooling the enclosure according to the plurality of candidate schedules, based on the thermal model, the energy cost model, and the environmental model.
3. The method of claim 1 or 2, wherein evaluating the seed schedule includes: comparing a cost of the seed schedule to costs of a portion of the plurality of candidate schedules.
4. The method of any one of claim 1-3, wherein the costs represent efficiency of the plurality of candidate schedules in controlling the refrigeration system.
5. The method of any one of claims 1-4, wherein the costs represent an energy cost, an energy consumption, or a combination of the energy cost and the energy consumption.
6. The method of any one of claims 1-5, wherein the operational schedule is determined for one or more points in time over the predetermined period of future time.
7. The method of any one of claims 1-6, further comprising: calibrating the multi-dimensional graph over time.
8. The method of any one of claims 1-7, wherein the iterative optimization algorithm includes gradient descent.
9. The method of any one of claims 1-8, wherein the thermal properties include at least one of a thermal capacity of content within the enclosure and a thermal resistance of the enclosure.
10. The method of any one of claims 1-9, wherein the external environmental conditions include at least one of temperature, humidity, precipitation, cloud cover, wind speed, and wind direction external to the enclosure.
11. The method of any one of claims 1-10, wherein the plurality of candidate schedules provides different levels of cooling of the enclosure at different points in time.
12. The method of claim 11, wherein the different levels of cooling include different levels of electric power for operating the refrigeration system.
13. A cold storage facility comprising: a cold storage enclosure defining a space for content; a refrigeration system configured to cool the enclosed space; a plurality of sensors configured to sense temperatures at locations within the enclosed space, and detect parameters of the refrigeration system; and a processor configured to perform operations comprising: determining a thermal model of the enclosure and the refrigeration system, the thermal model modeling one or more thermal properties of the enclosure and the refrigeration system under varied use and environmental conditions; obtaining an energy cost model, the energy cost model including a schedule of projected energy costs for a predetermined period of future time; obtaining an environmental model, the environmental model including one or more projected external environmental conditions in a geographic area where the enclosure is located for the predetermined period of future time; determining the operational schedule to control the refrigeration system over the predetermined period of future time by: generating a plurality of candidate schedules for controlling the refrigeration system for the predetermined period of future time, the plurality of candidate schedules determined based on using the thermal model, the energy cost model, and the environmental model; generating a multi-dimensional graph providing costs for cooling the enclosure according to the plurality of candidate schedules; randomly selecting a seed schedule from the plurality of candidate schedules; evaluating the seed schedule in the multi-dimensional graph using an iterative optimization algorithm; and selecting the operational schedule that provides an optimal cost from among the plurality of candidate schedules, the optimal cost corresponding to a local minimum of the costs identified when starting with the seed schedule in the multi-dimensional graph; and a controller configured to control the refrigeration system over the over the predetermined period of future time according to the determined operational schedule.
14. The cold storage facility of claim 13, where evaluating the seed schedule includes: comparing a cost of the seed schedule to costs of a portion of the plurality of candidate schedules.
15. The cold storage facility of claim 13 or 14, wherein the costs represent efficiency of the plurality of candidate schedules in controlling the refrigeration system.
16. The cold storage facility of any one of claims 13-15, wherein the costs represent an energy cost, an energy consumption, or a combination of the energy cost and the energy consumption.
17. The cold storage facility of any one of claims 13-16, wherein the operational schedule is determined for one or more points in time over the predetermined period of future time.
18. The cold storage facility of any one of claims 13-17, wherein the iterative optimization algorithm includes gradient descent.
19. The cold storage facility of any one of claims 13-18, wherein the plurality of candidate schedules provides different levels of cooling of the enclosure at different points in time, the different levels of cooling including different levels of electric power for operating the refrigeration system.
20. A cold storage management computer system for controlling a refrigeration system for an enclosure, the cold storage management computer system comprising: one or more processors; and memory storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising: receiving, from a control system, a request for an operational schedule for the refrigeration system; determining a thermal model of the enclosure and the refrigeration system, the thermal model modeling one or more thermal properties of the enclosure and the refrigeration system under varied use and environmental conditions; obtaining an energy cost model, the energy cost model including a schedule of projected energy costs for a predetermined period of future time; obtaining an environmental model, the environmental model including one or more projected external environmental conditions in a geographic area where the enclosure is located for the predetermined period of future time; determining the operational schedule to control the refrigeration system over the predetermined period of future time by: generating a plurality of candidate schedules for controlling the refrigeration system for the predetermined period of future time, the plurality of candidate schedules determined based on using the thermal model, the energy cost model, and the environmental model; generating a multi-dimensional graph providing costs for cooling the enclosure according to the plurality of candidate schedules; randomly selecting a seed schedule from the plurality of candidate schedules; evaluating the seed schedule in the multi-dimensional graph using an iterative optimization algorithm; and selecting the operational schedule that provides an optimal cost from among the plurality of candidate schedules, the optimal cost corresponding to a local minimum of the costs identified when starting with the seed schedule in the multi-dimensional graph.
21. A cold storage control system for controlling cooling of a cold storage facility, the cold storage control system comprising: one or more processors; an interface that transmits and receives data over one or more networks; one or more input ports configured to receive sensor signals from a plurality of sensors, the plurality of sensors configured to sense temperatures at locations within the cold storage facility, and detect parameters of a refrigeration system; one or more output ports configured to trigger operation of the refrigeration system configured to cool the cold storage facility; and memory storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising: transmitting, over the one or more networks, a request for an operational schedule for the refrigeration system; receiving, in response to the request, the operational schedule determined by: generating a plurality of candidate schedules for controlling the refrigeration system for the predetermined period of future time, the plurality of candidate schedules determined based on using the thermal model, the energy cost model, and the environmental model; generating a multi-dimensional graph providing costs for cooling the enclosure according to the plurality of candidate schedules; randomly selecting a seed schedule from the plurality of candidate schedules; evaluating the seed schedule in the multi-dimensional graph using an iterative optimization algorithm; and selecting the operational schedule that provides an optimal cost from among the plurality of candidate schedules, the optimal cost corresponding to a local minimum of the costs identified when starting with the seed schedule in the multi-dimensional graph; and a controller configured to control the refrigeration system over the predetermined period of future time according to the operational schedule.
GB2116805.9A 2019-04-22 2020-04-22 Scheduled thermal control system Active GB2598681B8 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US16/391,027 US10558937B1 (en) 2019-04-22 2019-04-22 Scheduled thermal control system
US16/724,801 US11367028B2 (en) 2019-04-22 2019-12-23 Scheduled thermal control system
PCT/US2020/029405 WO2020219608A1 (en) 2019-04-22 2020-04-22 Scheduled thermal control system

Publications (5)

Publication Number Publication Date
GB202116805D0 GB202116805D0 (en) 2022-01-05
GB2598681A true GB2598681A (en) 2022-03-09
GB2598681B GB2598681B (en) 2022-11-09
GB2598681B8 GB2598681B8 (en) 2024-05-15
GB2598681A8 GB2598681A8 (en) 2024-05-15

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US (4) US10558937B1 (en)
EP (1) EP3959672A1 (en)
CN (1) CN113906454B (en)
AU (3) AU2020261022B2 (en)
GB (1) GB2598681B8 (en)
NZ (2) NZ792090A (en)
WO (1) WO2020219608A1 (en)

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CN116817537B (en) * 2023-08-30 2023-11-03 江苏星星冷链科技有限公司 Multi-period refrigeration control method and system for refrigeration house based on external temperature measurement

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Also Published As

Publication number Publication date
US20240127139A1 (en) 2024-04-18
CN113906454A (en) 2022-01-07
GB2598681B8 (en) 2024-05-15
AU2020261022B2 (en) 2022-10-06
AU2022271416B2 (en) 2024-02-29
US11367028B2 (en) 2022-06-21
GB202116805D0 (en) 2022-01-05
EP3959672A1 (en) 2022-03-02
GB2598681B (en) 2022-11-09
US11790292B2 (en) 2023-10-17
NZ792090A (en) 2022-12-23
AU2020261022A1 (en) 2021-12-16
AU2024202842A1 (en) 2024-05-23
US20200334600A1 (en) 2020-10-22
NZ782432A (en) 2022-09-30
WO2020219608A1 (en) 2020-10-29
WO2020219608A8 (en) 2022-12-15
US10558937B1 (en) 2020-02-11
GB2598681A8 (en) 2024-05-15
US20220309422A1 (en) 2022-09-29
AU2022271416A1 (en) 2023-01-05
CN113906454B (en) 2023-10-17

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